"""simple docstring"""






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A        =     logging.get_logger(__name__)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Dict:
				SCREAMING_SNAKE_CASE__			=					torch.load(_lowercase	,       map_location="cpu"							)
				if "model" in sd.keys():
								SCREAMING_SNAKE_CASE__			=					torch.load(_lowercase	,       map_location="cpu"							)["model"]

				# pop unnecessary weights
				SCREAMING_SNAKE_CASE__			=					[
				    "decoder.version",
				    "decoder.output_projection.weight",
				]
				for key in keys_to_delete:
								if key in sd:
												sd.pop(_lowercase							)

				SCREAMING_SNAKE_CASE__			=					{
				    "decoder.project_in_dim.weight": "decoder.project_in.weight",
				    "decoder.project_out_dim.weight": "decoder.project_out.weight",
				    "decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
				    "decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
				}
				for old_key, new_key in keys_to_rename.items():
								if old_key in sd:
												SCREAMING_SNAKE_CASE__			=					sd.pop(_lowercase							)

				SCREAMING_SNAKE_CASE__			=					list(sd.keys()							)
				for key in keys:
								if ".qkv_proj." in key:
												SCREAMING_SNAKE_CASE__			=					sd[key]
												# We split QKV in separate Q,K,V

												SCREAMING_SNAKE_CASE__			=					key.replace(".qkv_proj."	,       ".q_proj."							)
												SCREAMING_SNAKE_CASE__			=					key.replace(".qkv_proj."	,       ".k_proj."							)
												SCREAMING_SNAKE_CASE__			=					key.replace(".qkv_proj."	,       ".v_proj."							)

												SCREAMING_SNAKE_CASE__			=					value.shape[0]
												assert depth % 3 == 0
												# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
												# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
												SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__			=					torch.split(_lowercase	,       depth // 3	,       dim=0							)

												SCREAMING_SNAKE_CASE__			=					q
												SCREAMING_SNAKE_CASE__			=					k
												SCREAMING_SNAKE_CASE__			=					v
												del sd[key]

				return sd




@torch.no_grad()
def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase=None							)				-> List[Any]:
				SCREAMING_SNAKE_CASE__			=					load_checkpoint(_lowercase							)

				if config is not None:
								SCREAMING_SNAKE_CASE__			=					OPTConfig.from_pretrained(_lowercase							)
				else:
								SCREAMING_SNAKE_CASE__			=					OPTConfig()

				SCREAMING_SNAKE_CASE__			=					OPTModel(_lowercase							).half().eval()
				model.load_state_dict(_lowercase							)

				# Check results
				Path(_lowercase							).mkdir(exist_ok=_lowercase							)
				model.save_pretrained(_lowercase							)


if __name__ == "__main__":
						_A        =     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--fairseq_path',
						    type=str,
						    help=(
						        'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
						        ' https://huggingface.co/models?other=opt_metasq'
						    ),
						)
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
						_A        =     parser.parse_args()
						convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








"""simple docstring"""



def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> Tuple:


	"""simple docstring"""




	lowerCAmelCase_      							=			len(_lowercase	)
	lowerCAmelCase_      							=			sum(_lowercase	)

	lowerCAmelCase_      							=			[[False for x in range(s + 1	)] for y in range(n + 1	)]

	for i in range(1			,   n + 1	):
		lowerCAmelCase_      							=			True

	for i in range(1			,   s + 1	):
		lowerCAmelCase_      							=			False

	for i in range(1			,   n + 1	):
		for j in range(1			,   s + 1	):
			lowerCAmelCase_      							=			dp[i][j - 1]

			if arr[i - 1] <= j:
				lowerCAmelCase_      							=			dp[i][j] or dp[i - 1][j - arr[i - 1]]

	for j in range(int(s / 2	)			,   -1			,   -1	):
		if dp[n][j] is True:
			lowerCAmelCase_      							=			s - 2 * j
			break

	return diff